Polymorphic computing abstraction for heterogeneous architectures

Integration of multiple computing paradigms onto system on chip (SoC) has pushed the boundaries of design space exploration for hardware architectures and computing system software stack. The heterogeneity of computing styles in SoC has created a new class of architectures referred to as Heterogeneous Architectures. Novel applications developed to exploit the different computing styles are user centric for embedded SoC. Software and hardware designers are faced with several challenges to harness the full potential of heterogeneous architectures. Applications have to execute on more than one compute style to increase overall SoC resource utilization. The implication of such an abstraction is that application threads need to be polymorphic. Operating system layer is thus faced with the problem of scheduling polymorphic threads. Resource allocation is also an important problem to be dealt by the OS. Morphism evolution of application threads is constrained by the availability of heterogeneous computing resources. Traditional design optimization goals such as computational power and lower energy per computation are inadequate to satisfy user centric application resource needs. Resource allocation decisions at application layer need to permeate to the architectural layer to avoid conflicting demands which may affect energy-delay characteristics of application threads. We propose Polymorphic computing abstraction as a unified computing model for heterogeneous architectures to address the above issues. Simulation environment for polymorphic applications is developed and evaluated under various scheduling strategies to determine the effectiveness of polymorphism abstraction on resource allocation. User satisfaction model is also developed to complement polymorphism and used for optimization of resource utilization at application and network layer of embedded systems

A Client-Centric Data Streaming Technique for Smartphones: An Energy Evaluation

With advances in microelectronic and wireless communication technologies, smartphones have computer-like capabilities in terms of computing power and communication bandwidth. They allow users to use advanced applications that used to be run on computers only. Web browsing, email fetching, gaming, social networking, and multimedia streaming are examples of wide-spread smartphone applications. Unsurprisingly, network-related applications are dominant in the realm of smartphones. Users love to be connected while they are mobile. Streaming applications, as a part of network-related applications, are getting increasingly popular. Mobile TV, video on demand, and video sharing are some popular streaming services in the mobile world. Thus, the expected operational time of smartphones is rising rapidly. On the other hand, the enormous growth of smartphone applications and services adds up to a significant increase in complexity in the context of computation and communication needs, and thus there is a growing demand for energy in smartphones. Unlike the exponential growth in computing and communication technologies, the growth in battery technologies is not keeping up with the rapidly growing energy demand of these devices. Therefore, the smartphone's utility has been severely constrained by its limited battery lifetime. It is very important to conserve the smartphone's battery power. Even though hardware components are the actual energy consumers, software applications utilize the hardware components through the operating system. Thus, by making smartphone applications energy-efficient, the battery lifetime can be extended. With this view, this work focuses on two main problems: i) developing an energy testing methodology for smartphone applications, and ii) evaluating the energy cost and designing an energy-friendly downloader for smartphone streaming applications. The detailed contributions of this thesis are as follows: (i) it gives a generalized framework for energy performance testing and shows a detailed flowchart that application developers can easily follow to test their applications; (ii) it evaluates the energy cost of some popular streaming applications showing how the download strategy that an application developer adopts may adversely affect the energy savings; (iii) it develops a model of an energy-friendly downloader for streaming applications and studies the effects of the downloader's parameters regarding energy consumption; and finally, (iv) it gives a mathematical model for the proposed downloader and validates it by means of experiments

Recent Trends in Communication Networks

In recent years there has been many developments in communication technology. This has greatly enhanced the computing power of small handheld resource-constrained mobile devices. Different generations of communication technology have evolved. This had led to new research for communication of large volumes of data in different transmission media and the design of different communication protocols. Another direction of research concerns the secure and error-free communication between the sender and receiver despite the risk of the presence of an eavesdropper. For the communication requirement of a huge amount of multimedia streaming data, a lot of research has been carried out in the design of proper overlay networks. The book addresses new research techniques that have evolved to handle these challenges

Modeling and Evaluating Energy Performance of Smartphones

With advances in hardware miniaturization and wireless communication technologies even small portable wireless devices have much communication bandwidth and computing power. These devices include smartphones, tablet computers, and personal digital assistants. Users of these devices expect to run software applications that they usually have on their desktop computers as well as the new applications that are being developed for mobile devices. Web browsing, social networking, gaming, online multimedia playing, global positioning system based navigation, and accessing emails are examples of a few popular applications. Mobile versions of thousands of desktop applications are already available in mobile application markets, and consequently, the expected operational time of smartphones is rising rapidly. At the same time, the complexity of these applications is growing in terms of computation and communication needs, and there is a growing demand for energy in smartphones. However, unlike the exponential growth in computing and communication technologies, in terms of speed and packaging density, battery technology has not kept pace with the rapidly growing energy demand of these devices. Therefore, designers are faced with the need to enhance the battery life of smartphones. Knowledge of how energy is used and lost in the system components of the devices is vital to this end. With this view, we focus on modeling and evaluating the energy performance of smartphones in this thesis. We also propose techniques for enhancing the energy efficiency and functionality of smartphones. The detailed contributions of the thesis are as follows: (i) we present a nite state machine based model to estimate the energy cost of an application running on a smartphone, and provide practical approaches to extract model parameters; (ii) the concept of energy cost pro le is introduced to assess the impact of design decisions on energy cost at an early stage of software design; (iii) a generic architecture is proposed and implemented for enhancing the capabilities of smartphones by sharing resources; (iv) we have analyzed the Internet tra c of smartphones to observe the energy saving potentials, and have studied the implications on the existing energy saving techniques; and nally, (v) we have provided a methodology to select user level test cases for performing energy cost evaluation of applications. All of our concepts and proposed methodology have been validated with extensive measurements on a real test bench. Our work contributes to both theoretical understanding of energy e ciency of software applications and practical methodologies for evaluating energy e ciency. In summary, the results of this work can be used by application developers to make implementation level decisions that affect the energy efficiency of software applications on smartphones. In addition, this work leads to the design and implementation of energy e cient smartphones

Exploiting frame coherence in real-time rendering for energy-efficient GPUs

The computation capabilities of mobile GPUs have greatly evolved in the last generations, allowing real-time rendering of realistic scenes. However, the desire for processing complex environments clashes with the battery-operated nature of smartphones, for which users expect long operating times per charge and a low-enough temperature to comfortably hold them. Consequently, improving the energy-efficiency of mobile GPUs is paramount to fulfill both performance and low-power goals. The work of the processors from within the GPU and their accesses to off-chip memory are the main sources of energy consumption in graphics workloads. Yet most of this energy is spent in redundant computations, as the frame rate required to produce animations results in a sequence of extremely similar images. The goal of this thesis is to improve the energy-efficiency of mobile GPUs by designing micro-architectural mechanisms that leverage frame coherence in order to reduce the redundant computations and memory accesses inherent in graphics applications. First, we focus on reducing redundant color computations. Mobile GPUs typically employ an architecture called Tile-Based Rendering, in which the screen is divided into tiles that are independently rendered in on-chip buffers. It is common that more than 80% of the tiles produce exactly the same output between consecutive frames. We propose Rendering Elimination (RE), a mechanism that accurately determines such occurrences by computing and storing signatures of the inputs of all the tiles in a frame. If the signatures of a tile across consecutive frames are the same, the colors computed in the preceding frame are reused, saving all computations and memory accesses associated to the rendering of the tile. We show that RE vastly outperforms related schemes found in the literature, achieving a reduction of energy consumption of 37% and execution time of 33% with minimal overheads. Next, we focus on reducing redundant computations of fragments that will eventually not be visible. In real-time rendering, objects are processed in the order they are submitted to the GPU, which usually causes that the results of previously-computed objects are overwritten by new objects that turn occlude them. Consequently, whether or not a particular object will be occluded is not known until the entire scene has been processed. Based on the fact that visibility tends to remain constant across consecutive frames, we propose Early Visibility Resolution (EVR), a mechanism that predicts visibility based on information obtained in the preceding frame. EVR first computes and stores the depth of the farthest visible point after rendering each tile. Whenever a tile is rendered in the following frame, primitives that are farther from the observer than the stored depth are predicted to be occluded, and processed after the ones predicted to be visible. Additionally, this visibility prediction scheme is used to improve Rendering Elimination’s equal tile detection capabilities by not adding primitives predicted to be occluded in the signature. With minor hardware costs, EVR is shown to provide a reduction of energy consumption of 43% and execution time of 39%. Finally, we focus on reducing computations in tiles with low spatial frequencies. GPUs produce pixel colors by sampling triangles once per pixel and performing computations on each sampling location. However, most screen regions do not include sufficient detail to require high sampling rates, leading to a significant amount of energy wasted computing the same color for neighboring pixels. Given that spatial frequencies are maintained across frames, we propose Dynamic Sampling Rate, a mechanism that analyzes the spatial frequencies of tiles and determines the best sampling rate for them, which is applied in the following frame. Results show that Dynamic Sampling Rate significantly reduces processor activity, yielding energy savings of 40% and execution time reductions of 35%.La capacitat de càlcul de les GPU mòbils ha augmentat en gran mesura en les darreres generacions, permetent el renderitzat de paisatges complexos en temps real. Nogensmenys, el desig de processar escenes cada vegada més realistes xoca amb el fet que aquests dispositius funcionen amb bateries, i els usuaris n’esperen llargues durades i una temperatura prou baixa com per a ser agafats còmodament. En conseqüència, millorar l’eficiència energètica de les GPU mòbils és essencial per a aconseguir els objectius de rendiment i baix consum. Els processadors de la GPU i els seus accessos a memòria són els principals consumidors d’energia en càrregues gràfiques, però molt d’aquest consum és malbaratat en càlculs redundants, ja que les animacions produïdes s¿aconsegueixen renderitzant una seqüència d’imatges molt similars. L’objectiu d’aquesta tesi és millorar l’eficiència energètica de les GPU mòbils mitjançant el disseny de mecanismes microarquitectònics que aprofitin la coherència entre imatges per a reduir els càlculs i accessos redundants inherents a les aplicacions gràfiques. Primerament, ens centrem en reduir càlculs redundants de colors. A les GPU mòbils, sovint s'empra una arquitectura anomenada Tile-Based Rendering, en què la pantalla es divideix en regions que es processen independentment dins del xip. És habitual que més del 80% de les regions de pantalla produeixin els mateixos colors entre imatges consecutives. Proposem Rendering Elimination (RE), un mecanisme que determina acuradament aquests casos computant una signatura de les entrades de totes les regions. Si les signatures de dues imatges són iguals, es reutilitzen els colors calculats a la imatge anterior, el que estalvia tots els càlculs i accessos a memòria de la regió. RE supera àmpliament propostes relacionades de la literatura, aconseguint una reducció del consum energètic del 37% i del temps d’execució del 33%. Seguidament, ens centrem en reduir càlculs redundants en fragments que eventualment no seran visibles. En aplicacions gràfiques, els objectes es processen en l’ordre en què son enviats a la GPU, el que sovint causa que resultats ja processats siguin sobreescrits per nous objectes que els oclouen. Per tant, no se sap si un objecte serà visible o no fins que tota l’escena ha estat processada. Fonamentats en el fet que la visibilitat tendeix a ser constant entre imatges, proposem Early Visibility Resolution (EVR), un mecanisme que prediu la visibilitat basat en informació obtinguda a la imatge anterior. EVR computa i emmagatzema la profunditat del punt visible més llunyà després de processar cada regió de pantalla. Quan es processa una regió a la imatge següent, es prediu que les primitives més llunyanes a el punt guardat seran ocloses i es processen després de les que es prediuen que seran visibles. Addicionalment, aquest esquema de predicció s’empra en millorar la detecció de regions redundants de RE al no afegir les primitives que es prediu que seran ocloses a les signatures. Amb un cost de maquinari mínim, EVR aconsegueix una millora del consum energètic del 43% i del temps d’execució del 39%. Finalment, ens centrem a reduir càlculs en regions de pantalla amb poca freqüència espacial. Les GPU actuals produeixen colors mostrejant els triangles una vegada per cada píxel i fent càlculs a cada localització mostrejada. Però la majoria de regions no tenen suficient detall per a necessitar altes freqüències de mostreig, el que implica un malbaratament d’energia en el càlcul del mateix color en píxels adjacents. Com les freqüències tendeixen a mantenir-se en el temps, proposem Dynamic Sampling Rate (DSR)¸ un mecanisme que analitza les freqüències de les regions una vegada han estat renderitzades i en determina la menor freqüència de mostreig a la que es poden processar, que s’aplica a la següent imatge..

Exploiting frame coherence in real-time rendering for energy-efficient GPUs

The computation capabilities of mobile GPUs have greatly evolved in the last generations, allowing real-time rendering of realistic scenes. However, the desire for processing complex environments clashes with the battery-operated nature of smartphones, for which users expect long operating times per charge and a low-enough temperature to comfortably hold them. Consequently, improving the energy-efficiency of mobile GPUs is paramount to fulfill both performance and low-power goals. The work of the processors from within the GPU and their accesses to off-chip memory are the main sources of energy consumption in graphics workloads. Yet most of this energy is spent in redundant computations, as the frame rate required to produce animations results in a sequence of extremely similar images. The goal of this thesis is to improve the energy-efficiency of mobile GPUs by designing micro-architectural mechanisms that leverage frame coherence in order to reduce the redundant computations and memory accesses inherent in graphics applications. First, we focus on reducing redundant color computations. Mobile GPUs typically employ an architecture called Tile-Based Rendering, in which the screen is divided into tiles that are independently rendered in on-chip buffers. It is common that more than 80% of the tiles produce exactly the same output between consecutive frames. We propose Rendering Elimination (RE), a mechanism that accurately determines such occurrences by computing and storing signatures of the inputs of all the tiles in a frame. If the signatures of a tile across consecutive frames are the same, the colors computed in the preceding frame are reused, saving all computations and memory accesses associated to the rendering of the tile. We show that RE vastly outperforms related schemes found in the literature, achieving a reduction of energy consumption of 37% and execution time of 33% with minimal overheads. Next, we focus on reducing redundant computations of fragments that will eventually not be visible. In real-time rendering, objects are processed in the order they are submitted to the GPU, which usually causes that the results of previously-computed objects are overwritten by new objects that turn occlude them. Consequently, whether or not a particular object will be occluded is not known until the entire scene has been processed. Based on the fact that visibility tends to remain constant across consecutive frames, we propose Early Visibility Resolution (EVR), a mechanism that predicts visibility based on information obtained in the preceding frame. EVR first computes and stores the depth of the farthest visible point after rendering each tile. Whenever a tile is rendered in the following frame, primitives that are farther from the observer than the stored depth are predicted to be occluded, and processed after the ones predicted to be visible. Additionally, this visibility prediction scheme is used to improve Rendering Elimination’s equal tile detection capabilities by not adding primitives predicted to be occluded in the signature. With minor hardware costs, EVR is shown to provide a reduction of energy consumption of 43% and execution time of 39%. Finally, we focus on reducing computations in tiles with low spatial frequencies. GPUs produce pixel colors by sampling triangles once per pixel and performing computations on each sampling location. However, most screen regions do not include sufficient detail to require high sampling rates, leading to a significant amount of energy wasted computing the same color for neighboring pixels. Given that spatial frequencies are maintained across frames, we propose Dynamic Sampling Rate, a mechanism that analyzes the spatial frequencies of tiles and determines the best sampling rate for them, which is applied in the following frame. Results show that Dynamic Sampling Rate significantly reduces processor activity, yielding energy savings of 40% and execution time reductions of 35%.La capacitat de càlcul de les GPU mòbils ha augmentat en gran mesura en les darreres generacions, permetent el renderitzat de paisatges complexos en temps real. Nogensmenys, el desig de processar escenes cada vegada més realistes xoca amb el fet que aquests dispositius funcionen amb bateries, i els usuaris n’esperen llargues durades i una temperatura prou baixa com per a ser agafats còmodament. En conseqüència, millorar l’eficiència energètica de les GPU mòbils és essencial per a aconseguir els objectius de rendiment i baix consum. Els processadors de la GPU i els seus accessos a memòria són els principals consumidors d’energia en càrregues gràfiques, però molt d’aquest consum és malbaratat en càlculs redundants, ja que les animacions produïdes s¿aconsegueixen renderitzant una seqüència d’imatges molt similars. L’objectiu d’aquesta tesi és millorar l’eficiència energètica de les GPU mòbils mitjançant el disseny de mecanismes microarquitectònics que aprofitin la coherència entre imatges per a reduir els càlculs i accessos redundants inherents a les aplicacions gràfiques. Primerament, ens centrem en reduir càlculs redundants de colors. A les GPU mòbils, sovint s'empra una arquitectura anomenada Tile-Based Rendering, en què la pantalla es divideix en regions que es processen independentment dins del xip. És habitual que més del 80% de les regions de pantalla produeixin els mateixos colors entre imatges consecutives. Proposem Rendering Elimination (RE), un mecanisme que determina acuradament aquests casos computant una signatura de les entrades de totes les regions. Si les signatures de dues imatges són iguals, es reutilitzen els colors calculats a la imatge anterior, el que estalvia tots els càlculs i accessos a memòria de la regió. RE supera àmpliament propostes relacionades de la literatura, aconseguint una reducció del consum energètic del 37% i del temps d’execució del 33%. Seguidament, ens centrem en reduir càlculs redundants en fragments que eventualment no seran visibles. En aplicacions gràfiques, els objectes es processen en l’ordre en què son enviats a la GPU, el que sovint causa que resultats ja processats siguin sobreescrits per nous objectes que els oclouen. Per tant, no se sap si un objecte serà visible o no fins que tota l’escena ha estat processada. Fonamentats en el fet que la visibilitat tendeix a ser constant entre imatges, proposem Early Visibility Resolution (EVR), un mecanisme que prediu la visibilitat basat en informació obtinguda a la imatge anterior. EVR computa i emmagatzema la profunditat del punt visible més llunyà després de processar cada regió de pantalla. Quan es processa una regió a la imatge següent, es prediu que les primitives més llunyanes a el punt guardat seran ocloses i es processen després de les que es prediuen que seran visibles. Addicionalment, aquest esquema de predicció s’empra en millorar la detecció de regions redundants de RE al no afegir les primitives que es prediu que seran ocloses a les signatures. Amb un cost de maquinari mínim, EVR aconsegueix una millora del consum energètic del 43% i del temps d’execució del 39%. Finalment, ens centrem a reduir càlculs en regions de pantalla amb poca freqüència espacial. Les GPU actuals produeixen colors mostrejant els triangles una vegada per cada píxel i fent càlculs a cada localització mostrejada. Però la majoria de regions no tenen suficient detall per a necessitar altes freqüències de mostreig, el que implica un malbaratament d’energia en el càlcul del mateix color en píxels adjacents. Com les freqüències tendeixen a mantenir-se en el temps, proposem Dynamic Sampling Rate (DSR)¸ un mecanisme que analitza les freqüències de les regions una vegada han estat renderitzades i en determina la menor freqüència de mostreig a la que es poden processar, que s’aplica a la següent imatge...Postprint (published version